MAE Seminar: Steven L. Brunton

Steven L. Brunton, Ph.D.

University of Washington

Data-Driven Discovery and Control of Complex Systems

Location: Scott Lab E525

Steven L. Brunton, Ph.D.
Steven L. Brunton is an associate professor of mechanical engineering at the University of Washington. He is also adjunct associate professor of applied mathematics and a data science Fellow at the eScience Institute. Brunton received a BS in mathematics from Caltech in 2006 and a PhD in mechanical and aerospace engineering from Princeton in 2012. His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, biolocomotion, optics, energy systems and manufacturing. He is a co-author of three textbooks, received the Army and Air Force Young Investigator Program awards and Army Early Career Award for Scientists and Engineers (ECASE), and he was awarded the University of Washington College of Engineering junior faculty and teaching awards.

Abstract
Accurate and efficient reduced-order models are essential to understand, predict, estimate and control complex, multiscale and nonlinear dynamical systems. These models should ideally be generalizable, interpretable and based on limited training data. This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. This perspective, combining dynamical systems with machine learning and sparse sensing, is explored with the overarching goal of real-time, closed-loop feedback control.  First, the presenter will discuss how it is possible to enforce known constraints, such as energy conserving quadratic nonlinearities in incompressible fluids, to “bake in” known physics.  Next, it will be demonstrated that higher-order nonlinearities can approximate the effect of truncated modes, resulting in more accurate models of lower order than Galerkin projection. Finally, the presenter will discuss how to design sparse sensors and design models based on intrinsic measurements of the system.  

Hosted by Professor Jack McNamara, Department of Mechanical and Aerospace Engineering and Aerospace Research Center.